Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection

TL;DR

This paper introduces a real-time deep learning pipeline using YOLOv8 for detecting the Migdal effect in high-resolution images, significantly reducing data volume while maintaining detection efficiency in dark matter searches.

Contribution

The paper presents a novel real-time object detection pipeline tailored for rare event searches in high-resolution scientific imaging, specifically enhancing Migdal effect detection in dark matter experiments.

Findings

Pipeline processes data faster than 120 fps camera rate

Reduces 20 million frames to about 1000 with minimal signal loss

Demonstrates feasibility of real-time rare event detection in high-throughput environments

Abstract

Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable of operating in real-time on high resolution images, attributing to their widespread usage across many fields. We present an end-to-end object detection pipeline designed for real-time rare event searches for the Migdal effect, using high-resolution image data from a state-of-the-art scientific CMOS camera in the MIGDAL experiment. The Migdal effect in nuclear scattering, crucial for sub-GeV dark matter searches, has yet to be experimentally confirmed, making its detection a primary goal of the MIGDAL experiment. Our pipeline employs the YOLOv8 object detection algorithm and is trained on real data to enhance the detection efficiency of nuclear and electronic recoils, particularly those exhibiting overlapping tracks that are indicative of the Migdal effect. When deployed online on the MIGDAL readout PC, we demonstrate our pipeline to process and perform the rare event search on 2D image data faster than the peak 120 frame per second acquisition rate of the CMOS camera. Applying these same steps offline, we demonstrate that we can reduce a sample of 20 million camera frames to around 1000 frames while maintaining nearly all signal that YOLOv8 is able to detect, thereby transforming a rare search into a much more manageable search. Our studies highlight the potential of pipelines similar to ours significantly improving the detection capabilities of experiments requiring rapid and precise object identification in high-throughput data environments.